FastAtlas: Real-Time Compact Atlases for Texture Space Shading (2502.17712v1)

Abstract: Texture-space shading (TSS) methods decouple shading and rasterization, allowing shading to be performed at a different framerate and spatial resolution than rasterization. TSS has many potential applications, including streaming shading across networks, and reducing rendering cost via shading reuse across consecutive frames and/or shading at reduced resolutions relative to display resolution. Real-time TSS shading requires texture atlases small enough to be easily stored in GPU memory. Using static atlases leads to significant space wastage, motivating real-time per-frame atlassing strategies that pack only the content visible in each frame. We propose FastAtlas, a novel atlasing method that runs entirely on the GPU and is fast enough to be performed at interactive rates per-frame. Our method combines new per-frame chart computation and parametrization strategies and an efficient general chart packing algorithm. Our chartification strategy removes visible seams in output renders, and our parameterization ensures a constant texel-to-pixel ratio, avoiding undesirable undersampling artifacts. Our packing method is more general, and produces more tightly packed atlases, than previous work. Jointly, these innovations enable us to produce shading outputs of significantly higher visual quality than those produced using alternative atlasing strategies. We validate FastAtlas by shading and rendering challenging scenes using different atlasing settings, reflecting the needs of different TSS applications (temporal reuse, streaming, reduced or elevated shading rates). We extensively compare FastAtlas to prior alternatives and demonstrate that it achieves better shading quality and reduces texture stretch compared to prior approaches using the same settings.

FastAtlas: Real-Time Compact Atlases for Texture Space Shading

The paper "FastAtlas: Real-Time Compact Atlases for Texture Space Shading" introduces a novel algorithm called FastAtlas, which is designed to optimize texture-space shading (TSS) by generating compact texture atlases efficiently in real-time. Unlike traditional methods, FastAtlas operates primarily on GPUs using parallel computation and focuses on increasing the visual quality of renders while minimizing resource usage like memory and computational time. Here is a detailed analysis of the paper's contributions, methodologies, and implications for future graphics rendering techniques.

Methodology

FastAtlas consists of several key technical advancements that collectively improve upon existing TSS methods:

1. Dynamic Chartification and Parameterization: The method dynamically generates and parameterizes charts per-frame, eliminating static atlas reliance that can lead to space wastage and inefficient memory usage. FastAtlas employs view-space projection of visible surfaces for texture parameterization, enabling a consistent texel-to-pixel ratio across charts.
2. Parallel Chart Packing Algorithm: To enhance packing efficiency, FastAtlas introduces a real-time GPU-based chart packing algorithm. This process leverages bucketing and cuckoo hashing for deterministic chart ordering and a parallel prefix-sum based folding strategy to optimize the packing within GPU constraints. The method avoids the excessive undersampling seen in methods like MSA and SAS [Mueller et al., 2018; Neff et al., 2022].
3. Seamless Texture Coordination: By defining atlas charts as connected visible components in each frame, FastAtlas eliminates visible seams, a common artifact in other TSS approaches where seams introduce rendering discontinuities. The direct use of visibility information streamlines allocation and resolves issues with undersampled seams.

Results and Comparisons

FastAtlas was benchmarked against both static assessment and state-of-the-art dynamic TSS methodologies (such as MSA-P and SAS). Key findings from the experiments are worth highlighting:

• Visual Quality: FastAtlas consistently reduced perceptual errors (F metric) compared to existing methods, achieving a 42%-57% improvement on mean error, especially when rendering high-resolution scenes with fixed atlas sizes such as $8K \times 8K$.
• Packing Efficiency and Stretch Metrics: The algorithm showed significant improvements in $L^{2}$ and $L^{\infty}$ texture stretch metrics, indicating more uniform sampling and superior control over texture distortions. These improvements are crucial for applications demanding visual fidelity, like VR.
• Runtime and Resource Usage: On standard desktop GPUs, FastAtlas achieved real-time performance with frame times averaging around 1.5ms for packing, demonstrating that the system scales well across different scenes and workload intensities. Compared to shader-heavy calculations, the reduced shading load enhanced overall performance efficiency.

Practical and Theoretical Implications

From a practical perspective, FastAtlas enhances the efficiency of real-time rendering applications, especially where memory and computational budgets are stringent. Applications such as game engines, virtual reality systems, and real-time streaming can benefit immensely from reduced shading rates enabled by compact atlases. The use of seamless charts also addresses persistent visual artifacts prevalent in conventional shading workflows.

Theoretically, FastAtlas points the way towards more adaptive shading systems that intelligently balance resources between visibility, memory, and computational load. Its use of projection-based strategies to mitigate texture stretching and aliasing could be explored in future work to address even finer granularity control in shading distributions.

Future Directions

Potential future avenues for research include exploring adaptive shading techniques that consider more complex lighting scenarios or multi-threading across CPU-GPU pipelines. Further refinement of the packing algorithm could yield faster runtimes or reduced memory usage, enhancing efficiency in systems with limited GPU capabilities. Moreover, integrating learning-based models that predict shading patterns could dynamically optimize real-time shading in ways not yet fully explored by current hardware.

Overall, FastAtlas represents a significant stride towards effective utilization of GPU capabilities in real-time texture space shading, warranting further exploration and extension across varying domains of interactive rendering systems.